Book Image

Big Data Analytics with R and Hadoop

By : Vignesh Prajapati
Book Image

Big Data Analytics with R and Hadoop

By: Vignesh Prajapati

Overview of this book

<p>Big data analytics is the process of examining large amounts of data of a variety of types to uncover hidden patterns, unknown correlations, and other useful information. Such information can provide competitive advantages over rival organizations and result in business benefits, such as more effective marketing and increased revenue. New methods of working with big data, such as Hadoop and MapReduce, offer alternatives to traditional data warehousing. <br /><br />Big Data Analytics with R and Hadoop is focused on the techniques of integrating R and Hadoop by various tools such as RHIPE and RHadoop. A powerful data analytics engine can be built, which can process analytics algorithms over a large scale dataset in a scalable manner. This can be implemented through data analytics operations of R, MapReduce, and HDFS of Hadoop.<br /><br />You will start with the installation and configuration of R and Hadoop. Next, you will discover information on various practical data analytics examples with R and Hadoop. Finally, you will learn how to import/export from various data sources to R. Big Data Analytics with R and Hadoop will also give you an easy understanding of the R and Hadoop connectors RHIPE, RHadoop, and Hadoop streaming.</p>

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Big Data Analytics with R and Hadoop
Vignesh Prajapati
Nov, 2013 | 238 pages
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Table of Contents (16 chapters)
Big Data Analytics with R and Hadoop
Big Data Analytics with R and Hadoop
Credits
Credits
About the Author
About the Author
Acknowledgment
Acknowledgment
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Getting Ready to Use R and Hadoop
Getting Ready to Use R and Hadoop
Installing R
Installing RStudio
Understanding the features of R language
Installing Hadoop
Understanding Hadoop features
Learning the HDFS and MapReduce architecture
Understanding Hadoop subprojects
Summary
2
Writing Hadoop MapReduce Programs
Writing Hadoop MapReduce Programs
Understanding the basics of MapReduce
Introducing Hadoop MapReduce
Understanding the Hadoop MapReduce fundamentals
Writing a Hadoop MapReduce example
Learning the different ways to write Hadoop MapReduce in R
Summary
3
Integrating R and Hadoop
Integrating R and Hadoop
Introducing RHIPE
Introducing RHadoop
Summary
4
Using Hadoop Streaming with R
Using Hadoop Streaming with R
Understanding the basics of Hadoop streaming
Understanding how to run Hadoop streaming with R
Exploring the HadoopStreaming R package
Summary
5
Learning Data Analytics with R and Hadoop
Learning Data Analytics with R and Hadoop
Understanding the data analytics project life cycle
Understanding data analytics problems
Summary
6
Understanding Big Data Analysis with Machine Learning
Understanding Big Data Analysis with Machine Learning
Introduction to machine learning
Supervised machine-learning algorithms
Unsupervised machine learning algorithm
Recommendation algorithms
Summary
7
Importing and Exporting Data from Various DBs
Importing and Exporting Data from Various DBs
Learning about data files as database
Understanding MySQL
Understanding Excel
Understanding MongoDB
Understanding SQLite
Understanding PostgreSQL
Understanding Hive
Understanding HBase
Summary
References
References
R + Hadoop help materials
R groups
Hadoop groups
R + Hadoop groups
Popular R contributors
Popular Hadoop contributors
Index
Index

Supervised machine-learning algorithms

In this section, we will be learning about supervised machine-learning algorithms. The algorithms are as follows:

  • Linear regression

  • Logistic regression

Linear regression

Linear regression is mainly used for predicting and forecasting values based on historical information. Regression is a supervised machine-learning technique to identify the linear relationship between target variables and explanatory variables. We can say it is used for predicting the target variable values in numeric form.

In the following section, we will be learning about linear regression with R and linear regression with R and Hadoop.

Here, the variables that are going to be predicted are considered as target variables and the variables that are going to help predict the target variables are called explanatory variables. With the linear relationship, we can identify the impact of a change in explanatory variables on the target variable.

In mathematics, regression can be formulated as...

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